More and more data can be collected from mobile devices such as mobile terminals like smartphones, and transactional feeds can be created based on the associated observations. However, these feeds are not self-containing in thoroughly, or even sufficiently, characterizing a mobile device user in question, although the feeds may admittedly tell some details about related, e.g. transaction-oriented, time-dependent (point in time) and contextual (event can be linked to attributes like location or weather) events like the user's movements during a course of daily life.
Second, when behavioral data or technical observations need to be processed, the present database and data processing solutions are non-optimized in the light of multiple factors such as processing speed, memory requirements, or the general availability of historical data and making it available for sophisticated further processing or statistical analysis.
Third, despite the fact there are, in principle, huge amounts of information available about people's life, contemporary systems unfortunately mostly dismiss the linkage between historical data/models and real-time data, i.e. the practical applications, and fail to ascertain that their technical implementation is feasible given widely available database, storage and data processing hardware.
Nevertheless, a number of prior art publication still describe how to collect data points, position the user, or to make contextual data points locally available to other applications of a mobile device. For example, a prior art publication WO2008118119 discloses a mobile device and a method for communicating positioning data of the mobile device to a server at a periodic interval, automatically generating in the mobile device, in response to the server, a present location profile associated with a present geographic location of the device, simultaneously generating, in the mobile device, a set of adjacent profiles provided by the server as being a direction away from the present geographic location of the mobile device, and refreshing in the mobile device, the present location profile and the set of adjacent profiles at the periodic interval.
Notwithstanding the various prior art solutions for storing mobile device-related events and in view of the foregoing there still exists room for improvement and a need to describe how especially multi-dimensional data in particular on human behaviour can be stored and processed through a layered mechanism, not only to optimize performance, or to enable more complex analysis procedures, but also to generate more meaningful semantic indicators and profiles out of the data, and to physically separate different abstraction levels for both technical and legal reasons.